Learner modelling through interactional data collection

David Alfter


University of Gothenburg




Learner modeling


Language learning individual experience

Learner differences

  • Prior knowledge
  • Learning speed
  • Learning style
  • Language background

Online learning platforms rarely personalized

Fixed progression path

Personalized learning environments

Collection of available tools


Collect data from learners using the platform

Profile: static

Learner model: dynamic

  • Track learner activity on platform
  • Continuous assessment
  • Stealth assessment



Education background

Language background

Swell project


Collaboration with teachers

Learner model

Data collected during interaction with learning platform

General data

Not learner-specific

  • Current time
  • Frequency of use
  • Time since last visit
  • Screen size of device
  • IP address
  • Geolocation

Multiple choice vocabulary exercise

  • Time taken
  • History
  • Final answer

Variables to collect

Spelling exercise

Spelling exercise

  • Frequent (L1 specific) misspellings
  • Difficult sounds
  • Diagnostic and prognostic

Hypothesis testing

Evaluation of automatically graded vocabulary list

Grading of new vocabulary items

Adaptive diagnostic test

Different exercise types

  • Bundled gaps
  • Vocabulary knowledge
  • Sentence rearrangement
  • Sentence composition
  • Multiple choice

Teacher evaluation

To infinity and beyond

  • Morphology
  • Grammar

Error analysis

Error groups

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